The overall aim of the proposed research is to develop a theoretical account of the processes underlying classification learning and performance. Particular attention will be paid to ill-defined concepts where the component features have less than perfect correlations with category membership. Proposed experiments use a variety of stimuli, category structures, and procedures in order to provide a broad empirical base to evaluate alternative theories. A key objective is to test predictions of a context theory of classification which proposes that categorization often is based not on category-level information but rather on the retrieval of specific item information. Consequently, many of the proposed experiments examine instance-category relationships to see how these two levels of information become integrated. Other experiments evaluate category learning in nonstandard, nonlaboratory situations to examine the generality of people's tendency to solve categorization tasks by active hypothesis testing. Related experiments use a simulated medical problem-solving situation to see how disparate sources of information are combined in categorization. By the end of the project period we aim to have a theory of categorization that will allow us to account for performance with ill-defined concepts across a range of situations.